Multi-target community discovering method integrating structure clustering and attributive classification

Abstract

The invention discloses a multi-target community discovering method integrating structure clustering and attributive classification. The method comprises the steps as follows: establishing a network adjacent matrix and an attribute matrix; establishing objective function modularity for measuring structure quality of community division; establishing objective function homogeneity for measuring attribute quality of the community division; initializing a network community division population; using cross and mutation operation to update the community division population; combining a mutated community division population and an external dominance population; finding all dominance community division in a final community division population. The method of the invention designs a function for balancing node attribute classification quality based on Shannon information entropy theory and models an attribute classification problem as an objective function optimization problem. A multi-objective optimization strategy is used to optimize a modularity function for balancing structure clustering quality and a homogeneity function for balancing attribute classification quality to obtain a group of community structures, which are suitable for different applications corresponding to different balances between structure clustering and attribute classification.

Description

technical field [0001] The invention belongs to the technical field of complex networks, and in particular relates to a multi-objective community discovery method integrating structural clustering and attribute classification in complex networks, which can be used for network function analysis and structure visualization. Background technique [0002] Community detection methods in complex networks are crucial for understanding the function of the network and visualizing the structure of the network, etc. Generally speaking, a community is a subset of the set of all individuals in the network, and the individuals in the set are similar and dissimilar to the individuals outside the subset. [0003] According to the literature search of the prior art, most community discovery methods only consider the network topology information, define the community as a collection of closely connected nodes, and use the method of structural clustering to divide the network by using the topo...

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Problems solved by technology

[0005] In order to solve the problem that it is difficult for existing methods to make full use of network structure and attribute information to discover diverse community structures, the purpose of this invention is to propose a multi-objective community discovery method that integrates structural clustering and attribute classification. The objective function of classification quality, using a multi-objective optimization strategy to simultaneously optimize structural quality and attribute quality, discovers diverse community structures corresponding to different balances of structure and attributes

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